# -*- coding: utf-8 -*-
#参考链接:https://blog.csdn.net/YE1215172385/article/details/79750703
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager
from sklearn import svm

"Novelty Detection" 
#xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
# Generate train data
X = 0.3 * np.random.randn(100, 2, 3, 4)
X_train = (np.r_[X + 2, X - 2, X+2, X-2]).reshape(2400, 4)
# Generate some regular novel observations
X = 0.3 * np.random.randn(20, 2, 3, 4)
X_test = np.r_[X + 2, X - 2, X+2, X-2].reshape(480, 4)
# Generate some abnormal novel observations
X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2, 3, 4)).reshape(120, 4)
 
# fit the model
clf = svm.OneClassSVM(kernel="rbf", degree=3, gamma=0.1, nu=0.001)
clf.fit(X_train)
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)
y_pred_outliers = clf.predict(X_outliers)
n_error_train = y_pred_train[y_pred_train == -1].size
n_error_test = y_pred_test[y_pred_test == -1].size
n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
 

print("train:%s\%s\n" % (str(n_error_train), str(y_pred_train.size)))
print("test:%s\%s\n" % (str(n_error_test), str(y_pred_test.size))
print(y_pred_outliers.size)
print(n_error_train)
print()
print(n_error_outliers)

## plot the line, the points, and the nearest vectors to the plane
#Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) 
#Z = Z.reshape(xx.shape)
# 
#plt.title("Novelty Detection")
#plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)  #绘制异常样本的区域
#a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred')  #绘制正常样本和异常样本的边界
#plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred')   #绘制正常样本的区域
#s = 40
#b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white', s=s, edgecolors='k')
#b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='blueviolet', s=s,
#                 edgecolors='k')
#c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='gold', s=s,
#                edgecolors='k')
#plt.axis('tight')
#plt.xlim((-5, 5))
#plt.ylim((-5, 5))
#plt.legend([a.collections[0], b1, b2, c],
#           ["learned frontier", "training observations",
#            "new regular observations", "new abnormal observations"],
#           loc="upper left",
#           prop=matplotlib.font_manager.FontProperties(size=11))
#plt.xlabel(
#    "error train: %d/200 ; errors novel regular: %d/40 ; "
#    "errors novel abnormal: %d/40"
#    % (n_error_train, n_error_test, n_error_outliers))
#plt.show()
















